A great deal of attention has been given to automated machine-learning techniques. One area of study focuses on automated classification of input items. For example, as the volume of digital data has exploded in recent years, there is significant demand for techniques to organize and sort such data in a manner that allows it to be useful for a specified purpose.
Automated classification of digital information has application in a number of different practical situations, including image recognition (e.g., identifying which photographs from among thousands or millions in a database include a picture of a face or a picture of a particular face), text classification (e.g., determining whether a particular e-mail message is spam based on its textual content), and the like.
Various approaches to automated classification problems have been attempted. These approaches include supervised techniques, such as Support Vector Machine (SVM) and Naïve Bayes, as well as unsupervised techniques, such as clustering algorithms. However, each such conventional technique has its own limitations, and additional improvements in performance are always desired.